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Smart Sensing Technology for Environmental Monitoring
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Smart Sensing Technology for Environmental Monitoring

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 7613

Special Issue Editors

Dr. Eduardo Pinilla-Gil

E-Mail Website
Guest Editor
Department of Analytical Chemistry, University of Extremadura. Av. de Elvas s/n, 06006 Badajoz, Spain
Interests: electrochemical sensors; environmental monitoring; simplification and miniaturisation of sensors; environmental pollution assessment
Dr. Elena Bernalte Morgado

E-Mail Website
Guest Editor
Faculty of Sciences and Engineering, Manchester Metropolitan University M1 5 GD Manchester, UK
Interests: electrochemical (bio)sensors; screen-printed electrodes; heavy metals; environmental monitoring; sensing integration in remote scenarios
Dr. María del Carmen Hurtado-Sánchez

E-Mail Website
Guest Editor
Department of Analytical Chemistry, University of Extremadura. Av. de Elvas s/n, 06006 Badajoz,Spain
Interests: environmental monitoring; fluorescent sensors; electrochemical analysis; sensors for air pollutants; environmental biomarkers

Special Issue Information

Dear Colleagues,

Tackling environmental monitoring requires more and more development of Smart Sensing Technology that is capable and versatile to collect, analyze, and provide real-time, accurate, and representative data about chemical and physical parameters of the environment, otherwise unachievable using traditional lab-based analytical systems. The importance of Smart Sensors lies in their ability to combine sensitive and selective detection of pollutants, a reliable self-generation and storage of data, and automatic interconnection of devices to enhance the accessibility of the remotely obtained information. Further, some other exciting features of those sensors include reducing the overall cost of the monitoring process, facilitating continuous data collection from suspected pollution scenarios and their surroundings, accelerating informed decision-making when needed, and enabling re-programming of preset specific analytical protocols. Therefore, Smart Sensing Technology for Environmental Monitoring emerges as an up-and-coming research area with so much potential to revolutionize the development of integrated devices for a better understanding of complex environmental processes.

Because of that, this Special Issue is intended to present the state-of-the-art research, development, and application of this cutting-edge sensing technology, targeting specifically the environmental monitoring field. We kindly invite contributions mainly to the following topics, although other possible transversal subjects could be taken into consideration:

  • Sensor materials and description of the sensing mechanism;
  • Interface circuitry and signal processing methods for environmental sensing applications;
  • Smart biosensor platforms;
  • Smart integrated sensing platforms, including wireless networking for data collection and analysis;
  • Signal interfacing and processing for smart sensors in the environment;
  • Case studies of real application of smart sensors for environmental analysis;
  • Application of smart sensors in pollutant emissions monitoring.

Research papers, short communications, and reviews are all welcome.

Dr. Eduardo Pinilla-Gil
Dr. Elena Bernalte Morgado
Dr. María del Carmen Hurtado-Sánchez
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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Research

18 pages, 21404 KiB  
Article
Monitoring of PM2.5 Concentrations by Learning from Multi-Weather Sensors
by Yuexia Wang and Zhihuo Xu
Sensors 2020, 20(21), 6086; https://doi.org/10.3390/s20216086 - 26 Oct 2020
Cited by 4 | Viewed by 2117
Abstract
This paper aims to monitor the ambient level of particulate matter less than 2.5 μm (PM2.5) by learning from multi-weather sensors. Over the past decade, China has established a high-density network of automatic weather stations. In contrast, the number of [...] Read more.
This paper aims to monitor the ambient level of particulate matter less than 2.5 μm (PM2.5) by learning from multi-weather sensors. Over the past decade, China has established a high-density network of automatic weather stations. In contrast, the number of PM monitors is much smaller than the number of weather stations. Since the haze process is closely related to the variation of meteorological parameters, it is possible and promising to calculate the concentration of PM2.5 by studying the data from weather sensors. Here, we use three machine learning methods, namely multivariate linear regression, multivariate nonlinear regression, and neural network, in order to monitor PM2.5 by exploring the data of multi-weather sensors. The results show that the multivariate linear regression method has the root mean square error (RMSE) of 24.6756 μg/m3 with a correlation coefficient of 0.6281, by referring to the ground truth of PM2.5 time series data; and the multivariate nonlinear regression method has the RMSE of 24.9191 μg/m3 with a correlation coefficient of 0.6184, while the neural network based method has the best performance, of which the RMSE of PM2.5 estimates is 15.6391 μg/m3 with the correlation coefficient of 0.8701. Full article
(This article belongs to the Special Issue Smart Sensing Technology for Environmental Monitoring)
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17 pages, 6434 KiB  
Article
Field Evaluation of Low-Cost Particulate Matter Sensors in Beijing
by Han Mei, Pengfei Han, Yinan Wang, Ning Zeng, Di Liu, Qixiang Cai, Zhaoze Deng, Yinghong Wang, Yuepeng Pan and Xiao Tang
Sensors 2020, 20(16), 4381; https://doi.org/10.3390/s20164381 - 5 Aug 2020
Cited by 26 | Viewed by 4501
Abstract
Numerous particulate matter (PM) sensors with great development potential have emerged. However, whether the current sensors can be used for reliable long-term field monitoring is unclear. This study describes the research and application prospects of low-cost miniaturized sensors in PM2.5 monitoring. We [...] Read more.
Numerous particulate matter (PM) sensors with great development potential have emerged. However, whether the current sensors can be used for reliable long-term field monitoring is unclear. This study describes the research and application prospects of low-cost miniaturized sensors in PM2.5 monitoring. We evaluated five Plantower PMSA003 sensors deployed in Beijing, China, over 7 months (October 2019 to June 2020). The sensors tracked PM2.5 concentrations, which were compared to the measurements at the national control monitoring station of the Ministry of Ecology and Environment (MEE) at the same location. The correlations of the data from the PMSA003 sensors and MEE reference monitors (R2 = 0.83~0.90) and among the five sensors (R2 = 0.91~0.98) indicated a high accuracy and intersensor correlation. However, the sensors tended to underestimate high PM2.5 concentrations. The relative bias reached −24.82% when the PM2.5 concentration was >250 µg/m3. Conversely, overestimation and high errors were observed during periods of high relative humidity (RH > 60%). The relative bias reached 14.71% at RH > 75%. The PMSA003 sensors performed poorly during sand and dust storms, especially for the ambient PM10 concentration measurements. Overall, this study identified good correlations between PMSA003 sensors and reference monitors. Extreme field environments impact the data quality of low-cost sensors, and future corrections remain necessary. Full article
(This article belongs to the Special Issue Smart Sensing Technology for Environmental Monitoring)
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